HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
[physics.chem-ph] Autorzy: Ge Sun, Gervasio Zaldivar, Yuan Tian i 5 więcej
Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a polymer-native AI framework that processes complete polymer descriptions through a three-level hierarchical graph architecture built on the G2RINS representation. HiPoly encodes stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, using physically motivated design principles that mirror the multi-scale nature of polymeric systems. The framework establishes an end-to-end AI-driven workflow from experimental formulation data to property prediction, generative molecul…
- Kategoria arxiv
- physics.chem-ph
- Kategoria
- physics.chem-ph